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Optimization Methods for Large-Scale Machine Learning
v1v2v3 (latest)

Optimization Methods for Large-Scale Machine Learning

15 June 2016
Léon Bottou
Frank E. Curtis
J. Nocedal
ArXiv (abs)PDFHTML

Papers citing "Optimization Methods for Large-Scale Machine Learning"

50 / 1,491 papers shown
Concept Drift Monitoring and Diagnostics of Supervised Learning Models
  via Score Vectors
Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors
Kungang Zhang
A. Bui
D. Apley
114
16
0
12 Dec 2020
Structured learning of rigid-body dynamics: A survey and unified view
  from a robotics perspective
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective
A. R. Geist
Sebastian Trimpe
AI4CE
366
25
0
11 Dec 2020
Recent Theoretical Advances in Non-Convex Optimization
Recent Theoretical Advances in Non-Convex Optimization
Marina Danilova
Pavel Dvurechensky
Alexander Gasnikov
Eduard A. Gorbunov
Sergey Guminov
Dmitry Kamzolov
Innokentiy Shibaev
353
104
0
11 Dec 2020
Stochastic Damped L-BFGS with Controlled Norm of the Hessian
  Approximation
Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation
Sanae Lotfi
Tiphaine Bonniot de Ruisselet
D. Orban
Andrea Lodi
ODL
98
6
0
10 Dec 2020
Asymptotic study of stochastic adaptive algorithm in non-convex
  landscape
Asymptotic study of stochastic adaptive algorithm in non-convex landscapeJournal of machine learning research (JMLR), 2020
S. Gadat
Ioana Gavra
249
21
0
10 Dec 2020
DONE: Distributed Approximate Newton-type Method for Federated Edge
  Learning
DONE: Distributed Approximate Newton-type Method for Federated Edge LearningIEEE Transactions on Parallel and Distributed Systems (TPDS), 2020
Canh T. Dinh
N. H. Tran
Tuan Dung Nguyen
Wei Bao
A. R. Balef
B. Zhou
Albert Y. Zomaya
FedML
415
18
0
10 Dec 2020
Adaptive Sequential SAA for Solving Two-stage Stochastic Linear Programs
Adaptive Sequential SAA for Solving Two-stage Stochastic Linear Programs
R. Pasupathy
Yongjia Song
143
1
0
07 Dec 2020
Block majorization-minimization with diminishing radius for constrained
  nonconvex optimization
Block majorization-minimization with diminishing radius for constrained nonconvex optimization
Hanbaek Lyu
Yuchen Li
531
10
0
07 Dec 2020
When Do Curricula Work?
When Do Curricula Work?International Conference on Learning Representations (ICLR), 2020
Xiaoxia Wu
Ethan Dyer
Behnam Neyshabur
386
128
0
05 Dec 2020
Characterization of Excess Risk for Locally Strongly Convex Population
  Risk
Characterization of Excess Risk for Locally Strongly Convex Population RiskNeural Information Processing Systems (NeurIPS), 2020
Mingyang Yi
Ruoyu Wang
Zhi-Ming Ma
344
4
0
04 Dec 2020
Learning with risks based on M-location
Learning with risks based on M-locationMachine-mediated learning (ML), 2020
Matthew J. Holland
234
10
0
04 Dec 2020
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style
  Adaptive Momentum
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum
Bao Wang
Qiang Ye
ODL
197
16
0
03 Dec 2020
Accumulated Decoupled Learning: Mitigating Gradient Staleness in
  Inter-Layer Model Parallelization
Accumulated Decoupled Learning: Mitigating Gradient Staleness in Inter-Layer Model Parallelization
Huiping Zhuang
Zhiping Lin
Kar-Ann Toh
316
4
0
03 Dec 2020
Convergence of Gradient Algorithms for Nonconvex C^{1+alpha} Cost
  Functions
Convergence of Gradient Algorithms for Nonconvex C^{1+alpha} Cost FunctionsChinese Annals of Mathematics. Series B (Chin. Ann. Math. Ser. B), 2020
Zixuan Wang
Shanjian Tang
155
0
0
01 Dec 2020
A Hypergradient Approach to Robust Regression without Correspondence
A Hypergradient Approach to Robust Regression without CorrespondenceInternational Conference on Learning Representations (ICLR), 2020
Yujia Xie
Yongyi Mao
Simiao Zuo
Hongteng Xu
X. Ye
T. Zhao
H. Zha
293
16
0
30 Nov 2020
Is Support Set Diversity Necessary for Meta-Learning?
Is Support Set Diversity Necessary for Meta-Learning?
Amrith Rajagopal Setlur
Oscar Li
Virginia Smith
247
17
0
28 Nov 2020
Sequential convergence of AdaGrad algorithm for smooth convex
  optimization
Sequential convergence of AdaGrad algorithm for smooth convex optimizationOperations Research Letters (ORL), 2020
Cheik Traoré
Edouard Pauwels
171
29
0
24 Nov 2020
SMG: A Shuffling Gradient-Based Method with Momentum
SMG: A Shuffling Gradient-Based Method with MomentumInternational Conference on Machine Learning (ICML), 2020
Trang H. Tran
Lam M. Nguyen
Quoc Tran-Dinh
337
23
0
24 Nov 2020
On the Convergence of Continuous Constrained Optimization for Structure
  Learning
On the Convergence of Continuous Constrained Optimization for Structure LearningInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Ignavier Ng
Sébastien Lachapelle
Nan Rosemary Ke
Damien Scieur
Kun Zhang
506
43
0
23 Nov 2020
Continuous-Time Convergence Rates in Potential and Monotone Games
Continuous-Time Convergence Rates in Potential and Monotone GamesSIAM Journal of Control and Optimization (SICON), 2020
Bolin Gao
Lacra Pavel
237
10
0
21 Nov 2020
Convergence Analysis of Homotopy-SGD for non-convex optimization
Convergence Analysis of Homotopy-SGD for non-convex optimization
Matilde Gargiani
Andrea Zanelli
Quoc Tran-Dinh
Moritz Diehl
Katharina Eggensperger
142
4
0
20 Nov 2020
On the asymptotic rate of convergence of Stochastic Newton algorithms
  and their Weighted Averaged versions
On the asymptotic rate of convergence of Stochastic Newton algorithms and their Weighted Averaged versionsComputational optimization and applications (Comput. Optim. Appl.), 2020
Claire Boyer
Antoine Godichon-Baggioni
173
25
0
19 Nov 2020
MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for
  Training Large Graph Convolutional Networks
MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks
Tao Huang
Yihan Zhang
Jiajing Wu
Junyuan Fang
Zibin Zheng
GNN
62
3
0
17 Nov 2020
Policy design in experiments with unknown interference
Policy design in experiments with unknown interference
Davide Viviano
Jess Rudder
452
11
0
16 Nov 2020
Accelerating Distributed SGD for Linear Regression using Iterative
  Pre-Conditioning
Accelerating Distributed SGD for Linear Regression using Iterative Pre-ConditioningConference on Learning for Dynamics & Control (L4DC), 2020
Kushal Chakrabarti
Nirupam Gupta
Nikhil Chopra
222
3
0
15 Nov 2020
Sparse Representations of Positive Functions via First and Second-Order
  Pseudo-Mirror Descent
Sparse Representations of Positive Functions via First and Second-Order Pseudo-Mirror DescentIEEE Transactions on Signal Processing (TSP), 2020
A. Chakraborty
K. Rajawat
Alec Koppel
360
3
0
13 Nov 2020
Convergence Properties of Stochastic Hypergradients
Convergence Properties of Stochastic HypergradientsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Riccardo Grazzi
Massimiliano Pontil
Saverio Salzo
481
28
0
13 Nov 2020
SALR: Sharpness-aware Learning Rate Scheduler for Improved
  Generalization
SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization
Xubo Yue
Maher Nouiehed
Raed Al Kontar
ODL
207
6
0
10 Nov 2020
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient
  Filtering
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering
Ricky T. Q. Chen
Dami Choi
Lukas Balles
David Duvenaud
Philipp Hennig
ODL
221
6
0
09 Nov 2020
Stochastic Approximation for High-frequency Observations in Data
  Assimilation
Stochastic Approximation for High-frequency Observations in Data Assimilation
Shushu Zhang
V. Patel
146
1
0
05 Nov 2020
Gradient-Based Empirical Risk Minimization using Local Polynomial
  Regression
Gradient-Based Empirical Risk Minimization using Local Polynomial Regression
Ali Jadbabaie
A. Makur
Devavrat Shah
564
8
0
04 Nov 2020
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient
  Flow
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow
Youssef Mroueh
Truyen V. Nguyen
209
26
0
04 Nov 2020
Quantized Variational Inference
Quantized Variational Inference
Amir Dib
88
1
0
04 Nov 2020
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and
  Finite-Time Performance
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance
Thinh T. Doan
308
56
0
03 Nov 2020
Asynchronous Parallel Stochastic Quasi-Newton Methods
Asynchronous Parallel Stochastic Quasi-Newton MethodsParallel Computing (PC), 2020
Qianqian Tong
Guannan Liang
Xingyu Cai
Chunjiang Zhu
J. Bi
ODL
233
10
0
02 Nov 2020
Adversarial Attacks on Optimization based Planners
Adversarial Attacks on Optimization based PlannersIEEE International Conference on Robotics and Automation (ICRA), 2020
Sai H. Vemprala
Ashish Kapoor
AAML
370
15
0
30 Oct 2020
Hogwild! over Distributed Local Data Sets with Linearly Increasing
  Mini-Batch Sizes
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch SizesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Marten van Dijk
Nhuong V. Nguyen
Toan N. Nguyen
Lam M. Nguyen
Quoc Tran-Dinh
Phuong Ha Nguyen
FedML
302
10
0
27 Oct 2020
Optimal Client Sampling for Federated Learning
Optimal Client Sampling for Federated Learning
Jiajun He
Samuel Horváth
Peter Richtárik
FedML
371
232
0
26 Oct 2020
Byzantine Resilient Distributed Multi-Task Learning
Byzantine Resilient Distributed Multi-Task LearningNeural Information Processing Systems (NeurIPS), 2020
Jiani Li
W. Abbas
X. Koutsoukos
203
9
0
25 Oct 2020
Demystifying Why Local Aggregation Helps: Convergence Analysis of
  Hierarchical SGD
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDAAAI Conference on Artificial Intelligence (AAAI), 2020
Jiayi Wang
Maroun Touma
Rong-Rong Chen
Mingyue Ji
FedML
297
75
0
24 Oct 2020
Sample Efficient Reinforcement Learning with REINFORCE
Sample Efficient Reinforcement Learning with REINFORCE
Junzi Zhang
Jongho Kim
Brendan O'Donoghue
Stephen P. Boyd
335
144
0
22 Oct 2020
How Data Augmentation affects Optimization for Linear Regression
How Data Augmentation affects Optimization for Linear Regression
Boris Hanin
Yi Sun
267
19
0
21 Oct 2020
Progressive Batching for Efficient Non-linear Least Squares
Progressive Batching for Efficient Non-linear Least Squares
Huu Le
Christopher Zach
E. Rosten
Oliver J. Woodford
153
4
0
21 Oct 2020
Dual Averaging is Surprisingly Effective for Deep Learning Optimization
Dual Averaging is Surprisingly Effective for Deep Learning Optimization
Samy Jelassi
Aaron Defazio
203
5
0
20 Oct 2020
On the Difficulty of Unbiased Alpha Divergence Minimization
On the Difficulty of Unbiased Alpha Divergence MinimizationInternational Conference on Machine Learning (ICML), 2020
Tomas Geffner
Justin Domke
392
18
0
19 Oct 2020
Factorization Machines with Regularization for Sparse Feature
  Interactions
Factorization Machines with Regularization for Sparse Feature InteractionsJournal of machine learning research (JMLR), 2020
Kyohei Atarashi
S. Oyama
M. Kurihara
244
10
0
19 Oct 2020
Bi-level Score Matching for Learning Energy-based Latent Variable Models
Bi-level Score Matching for Learning Energy-based Latent Variable Models
Fan Bao
Chongxuan Li
Kun Xu
Hang Su
Jun Zhu
Bo Zhang
185
15
0
15 Oct 2020
FedAT: A High-Performance and Communication-Efficient Federated Learning
  System with Asynchronous Tiers
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers
Zheng Chai
Yujing Chen
Ali Anwar
Bo Pan
Yue Cheng
Huzefa Rangwala
FedML
230
151
0
12 Oct 2020
AEGD: Adaptive Gradient Descent with Energy
AEGD: Adaptive Gradient Descent with EnergyNumerical Algebra, Control and Optimization (NACO), 2020
Hailiang Liu
Xuping Tian
ODL
283
12
0
10 Oct 2020
A variable metric mini-batch proximal stochastic recursive gradient
  algorithm with diagonal Barzilai-Borwein stepsize
A variable metric mini-batch proximal stochastic recursive gradient algorithm with diagonal Barzilai-Borwein stepsize
Tengteng Yu
Xinwei Liu
Yuhong Dai
Jie Sun
232
4
0
02 Oct 2020
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